Crack detection on concrete bridges is a critical task to ensure bridge safety. However, many cracks on concrete bridges show low contrast and blurry edges in practice, which brings challenges to image-based crack detection. In this article, to improve the detection accuracy of blurred cracks, we propose the HDCB-Net-a deep learning-based network with the hybrid dilated convolutional block (HDCB) for the pixel-level crack detection. Specifically, HDCB is employed to expand the receptive field of the convolution kernel without increasing the computational complexity and to avoid the gridding effect generated by the dilated convolution. Meanwhile, to achieve a reasonable efficiency/accuracy tradeoff, the HDCB-Net only contains a few downsampling stages, which can avoid the loss of blurred crack pixels due to excessive downsampling. Furthermore, a two-stage strategy is proposed to realize the fast crack detection in a massive number of images (more than 100 000) with the high resolution (5120 × 5120 pixels). At the first stage, YOLOv4 is employed to filter out images without cracks and generate coarse region proposals. At the second stage, to achieve refined damage analysis, the HDCB-Net is used to detect pixel-level cracks from the coarse region proposals. The experimental results demonstrate that the proposed HDCB-Net is genetic and able to improve the detection accuracy of blurred cracks, and our two-stage strategy is efficient for fast crack detection. The whole detection process takes only 0.64 s to handle a single image. Additionally, we have established a public dataset, including 150 632 high-resolution images, dedicated to the research of crack detection, which have been released along with this article.